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Sciences http://pos.sagepub.com hosted at http://online.sagepub.comEvidence, Inference, and PurposeJulian ReissErasmus University Rotterdam, the Netherlands

All univocal analyses of causation face counterexamples. An attractive

response to this situation is to become a pluralist about causal relationships. “Causal pluralism” is itself, however, a pluralistic notion. In this article, I argue in favor of pluralism about concepts of cause in the social sciences. The article will show that evidence for, inference from, and the purpose of causal claims are very closely linked.

Keywords: causation; pluralism; evidence; methodology

In recent years, philosophers have slowly come to realize that the mar-ginal benefit of continuing the quest for a monistic account of causation, anaccount that provides a characterization of a single set of features that dis-tinguishes all causal from noncausal relations, is very low indeed. Manyhave responded by becoming pluralists about causation in one way oranother (versions of causal pluralism are defended by Campaner andGalavotti 2007; Cartwright 1999, 2007; De Vreese 2006; Godfrey-Smithforthcoming; Hall 2004; Hitchcock 2003; Psillos forthcoming; Russo andWilliamson 2007; and Weber 2007, among others). Causal pluralism is, however, itself a pluralistic notion: there are many dif-ferent kinds of it, and different versions differ greatly with respect to plausibil-ity (for a classification, see Hitchcock 2007). In this article, I argue in favor ofpluralism about concepts of cause in the social sciences. The argumentproceeds by showing, first, that counterexamples to the different accounts of

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causation have led a number of researchers to become pluralists about evidence

for causal claims. This is a position that social scientists should find very attrac-tive because of the wealth of alternative methods used to establish causal claimsthroughout the social sciences. I then show how evidential pluralism leads topluralism about the concept of cause, at least prima facie. Next, I consider apossible rescue for the conceptual monist, namely, to claim that possessingcausal knowledge of one type allows the inference to other types of causalknowledge, thereby unifying prima facie different concepts. I reject thisattempt. Last, I show that social scientists’ different purposes require differenttypes of causal knowledge. In sum, evidence for, inference from, and purposeof causal claims are tied together very closely.

II

A large variety of accounts of causation, each aspiring to be a candidate for

the one true theory, can be found in the philosophical literature. The startingpoint for this article is the observation of what I take to be a fact: every accountof causation, when offered as a universal theory of what causation consists inor what we mean by the word cause, is false because it is subject to counterex-amples. In this section I give the reader a flavor of this fact. I will present kinds of counterexample for both the necessity and the suf-ficiency of definition provided by each account. While it is certainly the casethat each type of theory can be improved such that it ceases to be subject tomany specific counterexamples I list here, I claim on inductive grounds thatone can reformulate the counterexample in such a way that the new theoryfails. For more detail on each type of theory and the recalcitrance of the asso-ciated counterexample, the reader is referred to the pertinent literature.

analysis and many legal theorists frequently identify causation with someform of counterfactual dependence. For example, in his famous essay“Objective Possibility and Adequate Causation in Historical Explanation,”Max Weber (1905/1949, 171) wrote,

Rather, does the attribution of effects to causes take place through a process of thought which includes a series of abstractions. The first and decisive one occurs when we conceive of one or a few of the actual causal components as modified in a certain direction and then ask ourselves whether under the conditions which have been thus changed, the same effect . . . or some other effect “would be expected.”

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22 Philosophy of the Social Sciences

Weber’s essay is no historical anomaly. In recent years, one could witness

an explosion of contributions to so-called virtual, alternate, or “what if?” his-tory (e.g., Tetlock, Lebow, and Parker 2006; Cowley 1999), and while someof these are certainly written mostly for entertainment, others (see in particu-lar the collection of Tetlock and Belkin [1996], which includes a thoroughmethodological discussion of this technique) have genuine cognitive pur-poses: historians construct counterfactual scenarios, such as a United Kingdomin 1938 without appeasement policy, a Cuba crisis in which Kennedy showsgreater resolve, or a Persian victory at Salamis, to identify the cause or causesof certain events of interest (in these cases, the Second World War, the Sovietdeployment of missiles, the rise of the West). Likewise, economic historianssometimes identify the causes of singular events (e.g., 19th-century Americaneconomic growth) by counterfactually removing a potential causal factor(e.g., the introduction of the railroad) and examine whether the outcomewould have been different (Fogel 1964). In some cases, the set of qualitativecauses of an outcome of interest is uncontroversial, and the real issue lieswith which of that set has more (quantitative) explanatory relevance. RobertNorthcott’s (2008) analysis of “weighted causal explanations” also employsa counterfactual criterion. In many areas of the law, similarly, causes of events are identified usingthe “but for” or sine qua non criterion: the claimant has to prove that but forthe alleged conduct of the defendant, the harm would not have occurred (forcriminal law, see for example Card [2006]; for tort law, McBride andBagshaw [2005]; for a detailed criticism of this criterion, see Hart andHonoré [1985, chap. 5]). However, some well-known cases show that causation cannot be identi-fied with counterfactual dependence. Cases of so-called redundant causa-tion in which a number of potential causes compete in bringing about aneffect show that counterfactual dependence is not necessary for causation.If some factor C would have brought about Y in the absence of X but due toX’s presence was prevented from doing so, X can be a cause of Y, but Ywould have happened even in the absence of X. To show that counterfactual dependence is not sufficient for causation ismore subtle. Counterfactual statements are ambiguous in at least one impor-tant respect. Consider an example David Lewis (1979, 456) discusses:

Jim and Jack quarreled yesterday, and Jack is still hopping mad. We conclude that if Jim asked Jack for help today, Jack would not help him. But wait: Jim is a prideful fellow. He never would ask for help after such a quarrel; if Jim were to ask Jack for help today, there would have to have been no quarrel

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yesterday. In that case Jack would be his usual generous self. So if Jim asked Jack for help today, Jack would help him after all.

Lewis, and many others after him, resolve this tension by demanding thatcounterfactuals be nonbacktracking—evaluated by inserting a small mira-cle just before the cause obtains and changing nothing but the occurrenceof the cause and its effects. But as I argue in a series of papers, this is onlyone way of evaluating a counterfactual claim, and not necessarily the bestfor all purposes (see Reiss and Cartwright 2004; Reiss forthcoming). Inparticular, it seems that historians, when addressing questions of the kind“What would have happened to Y, had X not happened?” often ask whatconditions would have had to be present for X not to obtain and thus evalu-ate a backtracking counterfactual. Evaluating counterfactuals in this way,however, creates a series of counterexamples (Reiss forthcoming).

that uses Boolean algebra for the qualitative study of macro-social phenom-ena and has been applied to fields as wide-ranging as sociology, politicalscience, economics, and criminology (for a full list of applications, see thebibliographical database at www.compasss.org). It identifies causes of phe-nomena of interest (e.g., ethnic political mobilization among WesternEuropean minorities; see Ragin 1998) by first arranging all observedinstances (in this case, minorities) in a table and determining whether thephenomenon is present. Then a list of factors (in this case, size, linguisticability, wealth relative to core region, and population growth) is constructed,and it is noted whether each factor is present or absent. A factor is judged tobe a cause whenever it is a member of a group such that that group of factorsis always associated with the phenomenon of interest and no subgroup isalways associated with the phenomenon. In other words, a factor is judged tobe a cause whenever it is an INUS condition, that is, an insufficient but non-redundant part of an unnecessary but sufficient condition (Mackie 1974). That regularities are not sufficient for causation is demonstrated byMackie himself. It is easy to verify that the sounding of the Manchesterhooters at 5.00 p.m. is an INUS condition for the Londoners to leave workshortly thereafter, but of course the Londoners do not leave the factorybecause of the sound of the Manchester hooters (see Mackie 1974, 81-84).That regularities, even those of the complex INUS type, are not necessaryfor causation can be shown by considering indeterministic cases of causa-tion of the kind one finds in quantum-mechanical phenomena, at leastaccording to some interpretations.

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statistical analyses of data abound in quantitative social sciences such aseconometrics, quantitative sociology, and political science. Many of the appli-cations are based on the classical linear regression model or extensions of it:

yi = α + βxi + ui,

where y is the dependent variable, α and β are regression parameters, u is

an error term, and the subscript i = (1, 2, 3 . . . ) denotes the observationnumber. The main idea behind it is that causes should be correlated with theireffects (x and y are correlated if and only if β differs from zero in the regres-sion): if money is a genuine cause of nominal income, or the availability ofdrug addiction rehabilitation a genuine preventer of recidivism, the obser-vation of one of these variables should be informative about the likely valueof the other. It is clear, however, that not all correlated variables are alsorelated as cause and effect. To recite a philosophers’ stock example, achange in the barometer reading is informative about the occurrence of astorm, but both are in fact caused by a change in atmospheric pressure. Thestandard solution to this problem is to “hold fixed”—condition upon—certainbackground factors that may affect the probability of the putative effect. Inthe regression model, it means to include these background factors as addi-tional independent variables. Nevertheless, counterexamples are not difficult to find. Even though manygenuine causes will be correlated with their effects, some are not. If a factoraffects another via two independent routes, the individual causal influencescan cancel such that there is causation without correlation. Two monotoni-cally increasing time series can be correlated, even conditional on putativecommon causes, when there is no causal relationship between them (seeSober 1987, 2001). Monotonically increasing time series are said to be “non-stationary”; there are many other sources of nonstationarity, and the bulk oftime series in the social sciences has this property so that correlations areseldom indicative of a causal connection alone, if at all (see Reiss 2007b).Correlation is therefore neither necessary nor sufficient for causation.

Mechanistic accounts. Another connotation of causation social scientists

employ for a variety of purposes is mechanism. If X causes Y, we wouldexpect there to be a mechanism from X to Y such as the transmissionmechanism from changes in the money stock to nominal income or the“self-fulfilling prophecy” mechanism by which bad news may cause bank

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runs. The main idea is that causal relations between social variables can bedecomposed into parts such that it can be shown how the causal message istransmitted from cause to effect. A related method of causal inference in thesocial sciences has been called “process tracing.” Daniel Steel (2004, 67)summarizes it as follows:

Process tracing consists in presenting evidence for the existence of several preva- lent social practices that, when linked together, produce a chain of causation from one variable to another. A successful instance of process tracing, then, demon- strates the existence of a social mechanism connecting the variables of interest.

The trouble is that there are ranges of cases of apparent causation in whichno such mechanism can be found. In an example due to Ned Hall (2004), avillain poses a threat to an air traffic controller who was about to send a signalto two planes on a crash course. As it happens, the planes crash, because ofthe threat. But no process or mechanism connects the two events. In cases ofomissions, such as the failure of a government to protect its populationagainst floods, there is no connecting mechanism either—because there is noevent of the right kind to begin with. Such cases are of great importance inthe law, especially tort law and criminal law (see for instance Pundik 2007). Depending on how precisely to cash out the meaning of “mechanism ofthe appropriate kind,” there are various problems with sufficiency too.According to one understanding, a mechanism is merely a series of (spatio-temporally contiguous) events X, C1, C2, . . . Cn, Y such that the transitionfrom each element to the next is governed by one or more laws (see Little1991, 14). Here one may encounter problems due to the lack of transitivityof some such relations. A stock philosophical example is as follows: the fall-ing boulder causes me to duck, the ducking causes my survival, but the fall-ing boulder does not cause my survival. In the social sciences, thresholdeffects can pose problems of this kind. For example, it may happen that Xcauses Y in the sense that some changes in X affect Y, and Y causes Z in thatsense, but X does not cause Z because the changes that X induces in Y arenot large enough (i.e., remain below the threshold) for Y to affect Z.

Interventionist accounts. A final intuition about causation I want to dis-

cuss here is the idea that one can often use causal relationships as recipesfor change (e.g., Gasking 1955; Woodward 2003). If, say, money reallydoes cause nominal income, it should in principle be possible to use thatrelationship to stabilize the economy; or if addiction programs really doprevent recidivism, governments should be able to reduce the latter byinvesting in the former.

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26 Philosophy of the Social Sciences

Many social scientists think of this meaning when thinking about causa-tion. In their influential textbook, Thomas Cook and Donald Campbell(1979, 36) write,

The paradigmatic assertion in causal relationships is that manipulation of a

cause will result in the manipulation of an effect. This concept of cause has been implicit in all the foregoing examples, and philosophers claim that it reflects the way causation is understood in everyday language. Causation implies that by varying one factor I can make another vary.

The econometricians’ notion of superexogeneity is based on this conception

(see Engle, Hendry, and Richard 1983), and so is Kevin Hoover’s Causalityin Macroeconomics (2001). But not all causal relationships are manipulable by us to effect change inthis way. Especially relationships in the social world can be fragile in the sensethat no matter how “surgical” the intervention is, it will break after the inter-vention. The history of the Phillips curve (on a causal reading of it) illustratesthis issue: the inverse relationship between unemployment and inflation,which had been more or less stable for over a century and at the time wasunderstood as causal rather than epiphenomenal, broke down after attempts toexploit it for policy. Of course, one can always argue that the type of interven-tion used in this case was not “of the appropriate kind.” But as long as ourcausal knowledge is supposed to help with the cognitive and practical pur-poses we pursue, this response has little bite. In the social sciences, we requirereal rather than ideal interventions (pace, in particular, Woodward 2003). Similar problems beset the sufficiency of the condition. Though it canbe proved that invariance under an ideal intervention identifies causal rela-tionships in certain kinds of system (see Cartwright 2007, chap. 10), suchsystems are rare (at any rate, not all systems are of the right kind); in otherkinds of system, we will always have to make do with real rather than idealinterventions, and these may lead to spurious results. Clearly, for instance,if the intervention affects the putative effect via a route that does not gothrough the putative cause, a joint change in the two variables is not neces-sarily indicative of a causal relationship.

III The problem the mentioned counterexamples point to is a difficulty forthese accounts of causation to the extent that they are thought of as universal

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theories of causation, that is, as providing necessary and sufficient conditions

for causation. One possible and straightforward response is to loosen therelationship between causation and what one might call the “manifesta-tions” of causation such as counterfactual dependence, correlation, stabilityunder intervention, and so on. The manifestations of causation, accordingto this response, are not regarded as defining causation or as expressingcharacteristics universally associated with causal relationships but ratheras providing evidence or test conditions for the existence of causal relation-ships. The relation between causation and its manifestations can thus beregarded as roughly equivalent to the relation between theoretical entitiesin science and their observable counterparts. Few philosophers today wouldhold that theoretical entities are defined in terms of their observable mani-festations. Nevertheless, observations can be evidence on the basis of whichwe infer the existence of and facts about the unobservable theoreticalentity. And of course, there are different sources of evidence for theoreticalclaims, just as there are a number of different kinds of evidence for causalrelations. Some philosophers and social scientists are thus led to what onemight call evidential pluralism about causation (this term seems to be dueto Russo [2006]; however, I would also list John Gerring [2005], PaulThagard [1999], and Jon Williamson [Russo and Williamson 2007] as hold-ing this view). The idea behind evidential pluralism is that evidence of a variety ofkinds—say, probabilistic, mechanistic, regularity—can bear on a causalhypothesis and strengthen it. Especially when evidence from two or moredifferent sources speaks in favor of the hypothesis, our confidence in thehypothesis should be boosted. Given what was said above, the rationalebehind this kind of thinking is straightforward. Since any given method isfallible—as shown by the counterexamples to the various accounts—theepistemically responsible strategy is to bring as much evidence as possibleto bear on the hypothesis at stake, and confirmation from a number of inde-pendent methods is one and perhaps the only way to be reasonably confi-dent about the truth of the hypothesis. The idea, then, is pretty much likethe idea of “triangulation” in other parts of science. One way to deal withthe problem of unreliable measurement instruments is to try to use anumber of physically different instruments such that if the result persists itcannot be an artifact of any of the instruments used as it would be highlyunlikely that two or more physically different instruments produce the samekinds of artifacts.

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IV

While a lot is to be said about this form of pluralism about causal rela-tions it seems to involve an important presupposition that I will scrutinizein this section. The presupposition is that the connection between the con-cept of cause and its manifestations or test conditions must be loose enoughfor evidential pluralism to work. Evidential pluralism could not work ifevery evidential method defined its own concept because when movingfrom method to method we would in fact change the hypothesis to betested. If (say) “X causes Y (as supported by probabilistic evidence)” meanssomething different from “X causes Y (as supported by mechanistic evi-dence),” evidential pluralism does not get off the ground because instead ofhaving one hypothesis that is being supported by two sources of evidence,we in fact have two separately supported hypotheses. In other words, wecannot be operationalists about the concept of cause. Rather, we require anindependent concept of cause that, nevertheless, bears some systematicrelationship with different evidential methods. A version of this type of position is defended by Jon Williamson (2006a;but see also Russo and Williamson 2007; Russo 2006; Gerring 2005).Williamson believes that there is a single, independently understood con-cept of cause that can be employed in hypotheses scientists confirm on thebasis of the different evidential methods. He defends an epistemic theory ofcausation that takes an epistemology of rational belief as its starting point.Evidence determines which causal beliefs the agent should adopt. Thecausal relation is then given by the set of causal beliefs that an agent withtotal evidence should adopt (Russo and Williamson 2007; cf. Williamson2005, chap. 9, 2006a, 2006b, 2007). Thus, for example, an agent mightinitially believe that two variables are causally connected because of anobserved correlation; however, she later learns that there is no possiblemechanism in between the two variables and thereby is led to revise herearlier belief and so forth. Unfortunately, there is a problem with the combination of conceptualmonism and evidential pluralism: there are ranges of cases where it doesnot work. To see this, consider the causal hypothesis “Watching violent TVprograms causes violence” (the example is entirely fictional; I use it tomake a conceptual, not an empirical, point). Suppose, then, that we followthe strategy described above and first look for probabilistic evidence. Let usassume that the consumption of violent TV programs (X) and violence (Y)are indeed correlated and that all noncausal sources of correlation (such asnonstationarity) can be controlled for. For simplicity, let us further assume

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that there are good reasons to believe that causation does not run from Y toX. However, as is common in social science, not all common causes areknown or measurable, and thus we cannot distinguish between “X causesY” and “C affects both X and Y, and X does not cause Y directly,” where Cis a common cause, on the basis of probabilistic evidence alone. The evi-dential pluralist now has us turn to a different kind of evidence, such asmechanistic evidence, for help. Now, suppose we find such evidence. For instance, it may be possible tostudy some individuals with enough detail such that a psychological mech-anism, according to which, say, consumers identify with aggressive charac-ters and come to think of the depicted scenarios as realistic, which thenresults in more violent behavior in real-life situations, can be established.Does this confirm the initial hypothesis? In some sense, yes. But only if the meaning of the word cause in ourhypothesis is as ambiguous as “cause in some sense or other.” This isbecause what has been said so far about the case is entirely compatible withthe existence of a second psychological mechanism, present in other indi-viduals (say), such that in these individuals TV consumption acts as a deter-rent, resulting in lower violence. In the relevant population these twomechanisms might just cancel so that in that population the two variablesare uncorrelated. Of course, we still need to account for the correlation in the overallpopulation. In the example, this may be due to an unobserved commoncause such as, say, socioeconomic status. Within each socioeconomic stra-tum, TV consumption and violence are uncorrelated. This is because withineach stratum the influences from the positive and the negative mechanismcancel. The correlation in the total population is brought about by a com-mon cause, but we cannot learn this from the statistics because the commoncause is not measurable (or not measured). It may be argued that the situation described in the example is unlikelyto happen outside a philosopher’s armchair because a fair amount of exactcanceling has to occur, and the chances for that to take place are very low.Now this may well be so. But the point I am making here is conceptual, notempirical. It may be an empirical truth that normally when mechanismsoperate in a certain way, their operation will show up in statistical data, sothat the two kinds of causation go together. This is, however, an empiricaltruth that has to be discovered a posteriori, not a truth we should build intothe concept of causation.

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To repeat this point, suppose we start out with a vague idea of what“causes” in the hypothesis “Watching violent TV programs causes violence”means—nothing more than, say, “brings about,” “affects,” “is responsiblefor,” and its other cognates. Once we turn to evidential support for thehypothesis, the term acquires a more determinate meaning such as “in apopulation that is causally homogeneous with respect to violence, the vari-ables ‘TV consumption’ and ‘violence’ are correlated.” Another methoddefines another concept: establishing that there is a mechanism from TVconsumption to (greater) violence establishes just that: for some individuals,TV consumption and violent behavior are connected by a psychologicalmechanism. Of course, the two are not entirely unrelated: if this mechanismis the only one that connects the two variables, we would expect the varia-bles to be correlated as well. Likewise, if (in the relevant population) thistype of mechanism can be found in many more individuals than countervail-ing mechanisms, we would expect a correlation. But these are statisticalarguments, pertaining to populations, not individuals and have little to dowith the mechanistic understanding of “cause.” To summarize, evidential pluralism of the kind defended by Williamsonand others presupposes that evidence produced by different methods can bebrought to bear on the same causal claim. But this does not always seem pos-sible. In our example, the hypothesis we can hope to establish or reject on thebasis of statistical evidence is a probabilistic one: in a causally homogeneouspopulation, is violence correlated with the consumption of violent TV pro-grams? (Answer in the example: no.) Using mechanistic evidence, by con-trast, we can hope to establish or reject a mechanistic hypothesis: is there,in some individuals, a continuous mechanism from “input variable”—TVconsumption—to “output variable”—violence? (Answer in the example:yes.) Conceptual monism is therefore, at least prima facie, false.

Causal claims are associated with certain inference rules that the com-petent user of the claim is licensed to make. What I mean by “licensed tomake an inference” is that there are good reasons to believe that the infer-ence rules are reliable for the purposes envisaged by the user. If, for exam-ple, a user competently claims that a certain training program causes acertain educational achievement in the probabilistic sense, say, he is enti-tled to infer that the claim holds not only in the population studied but also

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in populations that differ in no causally relevant detail but that have not,thus far, been studied.1 As has been argued in the previous section, the meaning of causal claimsis constrained by the type of evidence put forward in their favor. Perhaps itis possible to lift that constraint to some extent by showing that although agiven causal claim was initially established on the basis of one type ofevidence, the inference rules a competent user is licensed to make are moreencompassing. For instance, it may be the case that if X is an INUS condi-tion for Y, then if a user claims that X causes Y in that sense, he is licensedto infer that there is also a mechanism between X and Y or that one canintervene on X to change Y. If what was said in section II is correct, how-ever, then this is not so. Here are some examples of inferences that are notautomatically licensed:

• Knowing that X makes a difference to Y does not automatically allow the

inference that there is a continuous process between X and Y. • Knowing that X and Y are connected by some causal process does not automatically allow the inference that X raises the probability of Y. • Knowing that X raises the probability of Y does not automatically allow the inference that we can control Y via X.

Let us go through these examples in slightly greater detail to show the rel-evance for causation in the social sciences.

1. Does knowing that X is a difference-maker to Y allow the inference

that there is a continuous process between the two variables? In a greatvariety of legal contexts as well as in many analyses of social phenomena,certain kinds of omission are regarded as causes. Negligence in civil law,for instance, requires the defendant to have caused the harm that happenedto the plaintiff and typically consists in a failure to act. There is, typically,no continuous process (under any reasonable understanding of the term)between an omission and its effect. In such legal inquiries, the same coun-terfactual concept of cause is at work as in historical contexts and analysesof world politics. Some U.S. Democrats, for instance, accuse the Bush 1. There is a danger to understand the qualifier “differs in no causally relevant detail” asexcusing any apparent violation of the claim and therefore rendering it empty—“X causes Yunless it doesn’t.” But causal claims have intended applications and purposes, and thereforescientists normally know what counts as a legitimate application and as causally relevant detail(Lange 2000). Importantly, if in the new population the correlation does not hold, there must bea good reason to believe that that factor is itself a cause of the putative effect (Cartwright2002).

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administration of having ignored early terrorism warnings and thereby

causing the 9/11 attacks. Even if they were right, it would be foolish to tryto find the mechanism that led from the ignoring to the attacks.

2. Does knowing that X and Y are connected by a causal process allow

the inference that X and Y are correlated? Although Steel (2004, 71-72)recommends “process tracing” as an aid to ameliorate the “problem ofconfounders” (the problem of distinguishing alternative causal hypothesesby statistical means), he expresses some doubts about the practical useful-ness of the method:

It is also important to recognize how modest the accomplishments claimed by

process tracing actually are. Without the aid of statistical data, the best one can hope to establish by means of process tracing is purely qualitative causal claims. For instance, in Malinowski’s example, all we can conclude is that there is at least one path through which the number of wives exerts a positive influence on wealth among Trobriand chiefs. Not only does this conclusion fail to specify anything about the strength of the influence generated by this mechanism, it does not even entail that the overall effect of the number of wives on wealth is positive. One would naturally presume that having more wives would mean having more members of the household to provide for, which would be expected to exert a downward influence on wealth. Clearly, statistical data concerning the average cost-benefit ratio in yams of acquiring additional wives would be needed to decide which of these two conflicting influences was predominant [italics added], and no such data are provided by Malinowski.

The overall influence can thus be positive or negative—but also nil.

3. Does knowing that X is a probabilistic cause of Y allow the inference

that we can manipulate X to control Y? The denial of this question is pre-cisely the essence of the Lucas critique. A way to paraphrase Lucas is to saythat the prevailing large-scale econometric models (of the 1960s) at bestprovide evidence for historical causal relations that are subject to changewhen the system is tampered with. Since the aggregate relations depend fortheir existence partly on the economic agents’ expectations, and policy inter-ventions may change the expectations, the aggregate relations may be dis-rupted by policy. This is, of course, just what happened historically. We therefore have at least four concepts of cause at work here: “differ-ence making,” “connecting by means of a continuous process,” “probabilityraising,” and “remaining invariant under intervention.” This is not to say that

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there are no cases in which the different concepts coincide. Even if econo-mists disagree about their understanding of the mechanism underlying theliquidity effect, say, there will be some mechanism that transports the causalmessage from increases in the money supply to the reduction in nominalinterest rates. In such cases, a broader range of inferences is licensed byapplying the concept. Perhaps the attitude advocated here is too cautious. Is it not the case thatthe different meanings of cause typically coincide, that they come apartonly in special situations, often constructed by philosophers? Williamsonseems to hold this view. In his defense of conceptual monism, he distin-guishes between an “inferential” and an “explanatory” use of “the” causalrelation and argues,

There is also the rather general use of beliefs to systematise one’s evidence: an agent’s beliefs should typically be able to offer some kind of explanation of her experience and evidence. For example, if the agent discovers that two events are probabilistically dependent, and she knows of no non-causal explanation of this dependence (the events are not known to be overlapping, for instance) then she should (tentatively) believe that some causal connection between the events gives rise to the dependence, because dependencies between physical events are typically explained causally. This sketch involves a lot of “typically”s, because none of these features of causality hold invariably; if they did, a more straightforward analysis of causality in terms of one or more of these features might be possible; yet “typically” is quite enough for causal beliefs to be useful from an inferential and explanatory point of view. (Williamson 2006a, 75)

He thus seems to be saying that although there are cases where there isprobabilistic raising but no mechanism and vice versa, typically the two gotogether, and therefore we are licensed to expect one if we have evidencefor the other. I would put the matter differently. At the level of semantics, there arevarious concepts of cause such as probability raising, mechanism and soforth. It may well be that different concepts apply to a given situation, butif they do so, this is a matter of empirical truth, not a matter of conceptualtruth. On the basis of experience, we discover that in a certain domain allor most probabilistic dependencies can be explained by reference to anunderlying causal mechanism (say). Discovering this empirical fact ismuch like discovering that various symptoms of a disease typically co-occur (such as nasal stuffiness, sore throat, hoarseness, and cough typicallyaccompany the common cold). Making such discoveries is enormously useful.But we cannot stop short of empirical investigation to make them.

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34 Philosophy of the Social Sciences

Importantly, having evidence in favor of a causal claim of one type does

not, pace Williamson, entitle the bearer of the evidence to the belief inanother type of causal claim, even tentatively. Using terminology I devel-oped in a different context (see Reiss 2008, chap. 1), I would say thatestablishing a causal claim of one type at best provides prima facie evi-dence in favor of the related claim that uses a different causal concept.Prima facie evidence is only then valid evidence if all alternative explana-tions of its existence can be ruled out. For instance, a correlation betweenX and Y is prima facie evidence in favor of the claim that X causes Y. But itis valid evidence only insofar as noncausal explanations of the correlationas well as alternative causal accounts can be ruled out. The alternative account in the case at hand is simply that we face a caseof causation where the different concepts do not coapply. And this can onlybe ruled out by testing the alternative causal claim in its own right, usingevidence tailored to that alternative claim. The upshot is, prima facie evi-dence gives merely a license to investigate; for a license to believe, validevidence is required.

VI

The value of investigating the truth of causal hypotheses lies in the

degree to which these claims help in realizing scientists’ purposes and inthe value of realizing these purposes. About the latter, I have nothing to sayin this article. But I do want to make some remarks about how causal claimshelp to attain social scientists’ cognitive and practical purposes.2 Social scientists pursue a variety of different purposes such as predictingevents of interest, explaining individual events or general phenomena, andcontrolling outcomes for policy. It is interesting to note that the language of“cause” is employed in all these contexts. Consider the following examplesfrom econometrics, statistics, history, and sociology. In econometrics, the notion of Granger causality, which is closely relatedto probabilistic accounts of causation, cashes out whether a time series helpsto predict another. In a standard textbook, the following is said about it: 2. A fascinating story could be told about why, at certain times and places, certain purposesseem to dominate at the expense of others and when, why, and how these preferences are revised.The current passion in social science to investigate explanatory mechanisms, for example, is prob-ably in large part due to the field’s frustration with earlier strong positivist tendencies. Unfortunately,there is no space here to pursue these matters. For the sake of this article, I just take some salientpurposes social scientists pursue as given and examine their connections with causation. For arudimentary defense of pluralism about the purposes of social science, see Reiss (2007a).

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Granger causality (a kind of statistical feedback) is absent when f(xt | xt-1, yt-1) equals f(xt | xt-1). The definition states that in the conditional distribution, lagged values of yt add no information to explanation of movements of xt beyond that provided by lagged values of xt itself. This concept is useful in the construction of forecasting models [italics added] (Greene 2000, 657)

That this notion relates to prediction rather than one of the many othersenses of causation is made plain by the following example, also taken froman econometrics textbook:

The study uses annual data on two variables: total U.S. production of eggs (EGGS) from 1930 to 1983 and total U.S. production of chickens (CHICKENS) for the same period. The test is simple. EGGS is regressed on lagged EGGS and lagged CHICKENS; if the coefficients on lagged CHICKENS are significant as a group, then chickens cause eggs. A sym- metric regression is then used to test whether eggs cause chickens. To con- clude that one of the two “came first,” it is necessary to find unidirectional causality, i.e., to reject the noncausality of one to the other and at the same time fail to reject the noncausality of the other to the one. Thurman and Fisher’s test results were dramatic. Using lags ranging from 1 to 4 years, they obtained a clear rejection of the hypothesis that eggs do not cause chickens, but were unable to reject the hypothesis that chickens do not cause eggs. Thus they were able to conclude that the egg came first! (Pindyck and Rubinfeld 1991, 218-19)

Of course, this story is told partially tongue-in-cheek. But it does illustrate

a serious point: econometricians use the notion of cause often to mark outpredictive relations, quite independently of whether or not other kinds ofcausal assertions (for example, about connecting mechanisms) are sup-ported as well. In this case, we would expect mechanisms to run both waysbut only eggs help to predict chickens. Econometricians and statisticians also use another notion of cause. Thisnotion picks out those relations that are stable under intervention or “auton-omous” in econometricians’ jargon. The statistician David Freedman (1997,62) distinguishes three uses of regressions:

• to summarize data, • to predict values of the dependent variable, and • to predict the results of interventions.

He then reserves the notion of cause to the third:

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36 Philosophy of the Social Sciences

Causal inference is different, because a change in the system is contemplated;

for example, there will be an intervention. Descriptive statistics tell you about the correlations that happen to hold in the data; causal models claim to tell you what will happen to Y if you change X.

Patterns in the data are deemed causal because they are useful for the pre-diction of the results of policy interventions. A further important purpose across the social sciences is explanation.Explanation, to be sure, is itself not a monolithic concept, and differentscientists pursue different explanatory ideals. Two major approaches char-acterize the historical sciences: the idiographic and the nomothetic.Historians leaning toward idiographic analysis focus on the explanation ofsingular events and regard those conditions as causes (often significantdecisions of rulers), without which the event of interest would not havehappened. Such a decision explains the event of interest in just this sense:the event would not have happened but for the decision. As mentionedabove, this “but-for” conception is also at work in the law. By contrast, nomothetically leaning historians focus on generalizationsand think of explanation as subsumption under covering law. These histori-ans consequently hold a regularity view of causation (for the two concep-tions of cause in history, see Goertz and Levy 2007). In other social sciences, most notably economics and sociology, an eventor pattern of events is sometimes regarded as explained only if the mecha-nism that generates the event or pattern is understood (for economics, seefor instance Elster 2007, chap. 2; for sociology, Hedström and Swedberg1999). Here we therefore find a mechanistic conception of cause. What kind of causal hypothesis should be investigated (and, in tandem,what kind of evidence should be sought) therefore is to be determined on thebasis of purpose pursued in the given context. For certain kinds of prediction,Granger causation is appropriate and thus probabilistic evidence. Explanationis itself a multifaceted concept, and different notions of explanation requirecounterfactual, regularity, or mechanistic concepts of cause and the associatedkind of evidence. Some kinds of policy require a concept of cause as invariantunder intervention and, again, evidence able to support this kind of relation.

VII

If the analysis provided in this article is correct, the news is not alto-gether that good. Although there are different kinds of evidence for causal

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Reiss / Causation in the Social Sciences 37

relationships, different kinds of evidence tend to support different types of

causal claim, a fact that ties evidence and type of causal claim togethervery tightly. This is unfortunate as we pursue many different purposes andit would be nice if we could establish that X causes Y and thereby behelped in realizing all our purposes. For instance, it would be nice if wecould base policies on probabilistic evidence or if we found a mechanismbetween X and Y infer that X makes a difference to Y. As a general rule,this will not work. To be sure, the different kinds of causal claim are some-times true of the same system, but whether that is so is an empirical ques-tion that has to be addressed, and answered supported by evidence, in itsown right.3 Perhaps there does remain an open issue. Why do we call all these differ-ent relationships causal, and if they are really different, can one not at leastdescribe systematic connections between them? Perhaps this does stand inneed of explanation, but I cannot see systematic connections between themsave being useful in the light of certain types of purposes. And why we havecome to call the different kinds of relationships causal is a matter of histori-cal, not philosophical, inquiry. What about Williamson’s observation that neither scientists nor ordi-nary folk usually distinguish between the different senses of “cause” byqualifying “X probabilistically causes Y,” “Z mechanistically causes W,”and so on? I do agree with the observation. Unlike Williamson, however, Iwould not take it as evidence for conceptual monism. Rather, I think thatthe equivocation has often proved to be a hindrance to successful socialscience and policy. It is pretty much as Francis Bacon said more than 400years ago:

Although we think we govern our words, . . . certain it is that words, as a

Tartar’s bow, do shoot back upon the understanding of the wisest, and might- ily entangle and pervert the judgment. So that it is almost necessary, in all controversies and disputations, to imitate the wisdom of the mathematicians, in setting down in the very beginning the definitions of our words and terms, that others may know how we accept and understand them, and whether they concur with us or no. For it cometh to pass, for want of this, that we are sure to end there where we ought to have begun—in questions and differences about words. (Bacon 1605/2001, 126)

3. Nancy Cartwright makes a related point by lamenting the fact that we do not have a“theory of causality,” by which she means a systematization of the connections between thedifferent concepts of cause (see Cartwright 2007, chap. 4).

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Lange, Marc. 2000. Natural laws in scientific practice. Oxford: Oxford University Press.Lewis, David. 1979. Counterfactual dependence and time’s arrow. Noûs 13 (4): 455-76.Little, Daniel. 1991. Varieties of social explanation. Boulder, CO: Westview.Lucas, Robert. 1976. Econometric policy evaluation: A critique. Carnegie-Rochester Series on Public Policy 1:19-46.Mackie, John. 1974. The cement of the universe: A study of causation. Oxford: Oxford University Press.McBride, Nicholas, and Roderick Bagshaw. 2005. Tort law. 2nd ed. Harlow, UK: Longman.Northcott, Robert. 2008. Weighted explanations in history. Philosophy of the Social Sciences 38 (1): 76-96.Pindyck, Robert, and Daniel Rubinfeld. 1991. Econometric models and economic forecasts. New York: McGraw-Hill.Psillos, Stathis. Forthcoming. Causal pluralism. Athens, Greece: University of Athens Press.Pundik, Amit. 2007. Can one deny both causation by omission and causal pluralism? The case of legal causation. In Causality and probability in the sciences, edited by Federica Russo and Jon Williamson, 379-412. London: College Publications.Ragin, Charles. 1998. The logic of quality comparative analysis. International Review of Social History 43 (Suppl.): 105-24.Reiss, Julian. 2007a. Do we need mechanisms in the social sciences? Philosophy of the Social Sciences 37 (2): 163-84.———. 2007b. Time series, nonsense correlations and the principle of the common cause. In Causality and probability in the sciences, edited by Federica Russo and Jon Williamson, 179-96. London: College Publications.———. 2008. Error in economics: Towards a more evidence-based methodology. London: Routledge.———. Forthcoming. Counterfactuals, thought experiments and singular causal analysis in history. Philosophy of Science.Reiss, Julian, and Nancy Cartwright. 2004. Uncertainty in econometrics: Evaluating policy counterfactuals. In Economic policy under uncertainty: The role of truth and accountabil- ity in policy advice, edited by Peter Mooslechner, Helene Schuberth, and Martin Schürz, 204-32. Cheltenham, UK: Edward Elgar.Russo, Federica. 2006. The rationale of variation in methodological and evidential pluralism. Philosophica 77 (1): 97-123.Russo, Federica, and Jon Williamson. 2007. Interpreting causality in the health sciences. International Studies in the Philosophy of Science 21 (2): 157-70.Sober, Elliott. 1987. The principle of the common cause. In Probability and causation: Essays in honor of Wesley Salmon, edited by James Fetzer, 211-228. Dordrecht: Reidel.———. 2001. Venetian sea levels, British bread prices, and the principle of the common cause. British Journal for the Philosophy of Science 52:331-346.Steel, Daniel. 2004. Social mechanisms and causal inference. Philosophy of the Social Sciences 34 (1): 55-78.Tetlock, Philip, and Aaron Belkin, eds. 1996. Counterfactual thought experiments in world politics: Logical, methodological and psychological perspectives. Princeton, NJ: Princeton University Press.Tetlock, Philip, Richard Ned Lebow, and Geoffrey Parker. 2006. Unmaking the West: “What-if” scenarios that rewrite world history. Ann Arbor: University of Michigan Press.

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